vbatts/llama.cpp
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improve graph splitting, partial fix for --no-kv-offload

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This commit is contained in:
slaren 2024-01-06 00:58:22 +01:00
parent d107459321
commit ece0b0d855
3 changed files with 127 additions and 41 deletions
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132
ggml-backend.c
View file

@ -737,9 +737,16 @@ struct ggml_backend_sched_split {
int i_end;
struct ggml_tensor * inputs[GGML_MAX_SPLIT_INPUTS];
int n_inputs;
// graph view of this split
struct ggml_cgraph graph;
};
// TODO: group all the hash values into a single struct for clarity
//struct sched_hash_value {
// ggml_tallocr_t tallocr;
// struct ggml_tensor * copies[GGML_MAX_BACKENDS];
//};
struct ggml_backend_sched {
int n_backends;
ggml_backend_t backends[GGML_MAX_BACKENDS];
@ -747,11 +754,15 @@ struct ggml_backend_sched {
ggml_gallocr_t galloc;
// hash keys of the nodes in the graph
struct ggml_hash_set hash_set;
ggml_tallocr_t * node_talloc; // [hash_set.size]
struct ggml_tensor * (* node_copies)[GGML_MAX_BACKENDS]; // [hash_set.size][GGML_MAX_BACKENDS]
// hash values (arrays of [hash_set.size])
ggml_tallocr_t * node_talloc; // tallocr assigned to each node (indirectly this is the backend)
struct ggml_tensor * (* node_copies)[GGML_MAX_BACKENDS]; // copies of each node for each destination backend
// copy of the graph with modified inputs
struct ggml_cgraph * graph;
struct ggml_backend_sched_split splits[GGML_MAX_SPLITS];
int n_splits;
@ -928,6 +939,12 @@ static struct ggml_tensor * ggml_dup_tensor_layout(struct ggml_context * ctx, co
return dup;
}
//#define DEBUG_PASS1
//#define DEBUG_PASS2
//#define DEBUG_PASS3
//#define DEBUG_PASS4
// assigns backends to ops and splits the graph into subgraphs that can be computed on the same backend
// TODO: merge passes
static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
@ -977,14 +994,70 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g
node_allocr(node) = ggml_backend_sched_get_tallocr(sched, node_backend);
}
}
//printf("PASS 1 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
#ifdef DEBUG_PASS1
fprintf(stderr, "PASS 1 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
#endif
// pass 2: assign backends to ops from current assignments
// start from the end and assign the same backend to previous ops
// expand gpu backends (ie non last prio) up and down, ignoring cpu
// thus, cpu will never be used unless weights are on cpu, or there are no gpu ops between cpu ops
// pass 2.1 expand gpu up
{
ggml_tallocr_t cur_allocr = NULL;
for (int i = graph->n_nodes - 1; i >= 0; i--) {
struct ggml_tensor * node = graph->nodes[i];
if (ggml_is_view_op(node->op)) {
continue;
}
ggml_tallocr_t node_allocr = node_allocr(node);
if (node_allocr != NULL) {
if (sched_allocr_prio(sched, node_allocr) == sched->n_backends - 1) {
cur_allocr = NULL;
}
else {
cur_allocr = node_allocr;
}
} else {
node_allocr(node) = cur_allocr;
SET_CAUSE(node, "2.cur");
}
}
}
// pass 2.2 expand gpu down
{
ggml_tallocr_t cur_allocr = NULL;
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
if (ggml_is_view_op(node->op)) {
continue;
}
ggml_tallocr_t node_allocr = node_allocr(node);
if (node_allocr != NULL) {
if (sched_allocr_prio(sched, node_allocr) == sched->n_backends - 1) {
cur_allocr = NULL;
}
else {
cur_allocr = node_allocr;
}
} else {
node_allocr(node) = cur_allocr;
SET_CAUSE(node, "2.cur");
}
}
}
// pass 2.3 expand rest up
{
ggml_tallocr_t cur_allocr = NULL;
for (int i = graph->n_nodes - 1; i >= 0; i--) {
struct ggml_tensor * node = graph->nodes[i];
if (ggml_is_view_op(node->op)) {
continue;
}
ggml_tallocr_t node_allocr = node_allocr(node);
if (node_allocr != NULL) {
cur_allocr = node_allocr;
@ -994,13 +1067,18 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g
}
}
}
#ifdef DEBUG_PASS2
fprintf(stderr, "PASS 2 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
#endif
//printf("PASS 2 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
// pass 3: assign backends to remaining src from dst (should only be leafs)
// pass 3: assign backends to remaining src from dst and view_src
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
ggml_tallocr_t node_allocr = node_allocr(node);
ggml_tallocr_t cur_allocr = node_allocr(node);
if (ggml_is_view_op(node->op) && cur_allocr == NULL) {
cur_allocr = node_allocr(node) = node_allocr(node->view_src);
SET_CAUSE(node, "3.vsrc");
}
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * src = node->src[j];
if (src == NULL) {
@ -1008,11 +1086,18 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g
}
ggml_tallocr_t src_allocr = node_allocr(src);
if (src_allocr == NULL) {
node_allocr(src) = node_allocr;
if (src->view_src != NULL) {
// views are always on the same backend as the source
node_allocr(src) = node_allocr(src->view_src);
} else {
node_allocr(src) = cur_allocr;
}
}
}
}
//printf("PASS 3 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
#ifdef DEBUG_PASS3
fprintf(stderr, "PASS 3 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
#endif
// pass 4: split graph, find tensors that need to be copied
{
@ -1074,7 +1159,7 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g
sched->splits[cur_split].inputs[n_inputs] = (struct ggml_tensor *)src;
}
// create copies
// create a copy of the input in the split's backend
size_t id = hash_id(src);
if (sched->node_copies[id][cur_backend_id] == NULL) {
struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
@ -1090,8 +1175,9 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g
sched->splits[cur_split].i_end = graph->n_nodes;
sched->n_splits = cur_split + 1;
}
//fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
#ifdef DEBUG_PASS4
fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
#endif
#ifndef NDEBUG
// sanity check: all sources should have the same backend as the node
@ -1101,6 +1187,11 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g
if (node_allocr == NULL) {
fprintf(stderr, "!!!!!!! %s has no backend\n", node->name);
}
if (node->view_src != NULL && node_allocr != node_allocr(node->view_src)) {
fprintf(stderr, "!!!!!!! %s has backend %s, view_src %s has backend %s\n",
node->name, node_allocr ? ggml_backend_name(get_allocr_backend(sched, node_allocr)) : "NULL",
node->view_src->name, node_allocr(node->view_src) ? ggml_backend_name(get_allocr_backend(sched, node_allocr(node->view_src))) : "NULL");
}
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * src = node->src[j];
if (src == NULL) {
@ -1112,8 +1203,14 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g
node->name, node_allocr ? ggml_backend_name(get_allocr_backend(sched, node_allocr)) : "NULL",
j, src->name, src_allocr ? ggml_backend_name(get_allocr_backend(sched, src_allocr)) : "NULL");
}
if (src->view_src != NULL && src_allocr != node_allocr(src->view_src)) {
fprintf(stderr, "!!!!!!! [src] %s has backend %s, view_src %s has backend %s\n",
src->name, src_allocr ? ggml_backend_name(get_allocr_backend(sched, src_allocr)) : "NULL",
src->view_src->name, node_allocr(src->view_src) ? ggml_backend_name(get_allocr_backend(sched, node_allocr(src->view_src))) : "NULL");
}
}
}
fflush(stderr);
#endif
// create copies of the graph for each split
@ -1127,6 +1224,7 @@ static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * g
for (int j = 0; j < split->n_inputs; j++) {
struct ggml_tensor * input = split->inputs[j];
struct ggml_tensor * input_cpy = sched->node_copies[hash_id(input)][sched_allocr_prio(sched, split->tallocr)];
// add a dependency to the input source so that it is not freed before the copy is done
input_cpy->src[0] = input;
graph_copy->nodes[graph_copy->n_nodes++] = input_cpy;
}
@ -1163,19 +1261,20 @@ static void sched_compute_splits(ggml_backend_sched_t sched) {
struct ggml_tensor * input = split->inputs[j];
struct ggml_tensor * input_cpy = sched->node_copies[hash_id(input)][sched_backend_prio(sched, split_backend)];
if (input->buffer == NULL) {
GGML_ASSERT(false);
if (input->view_src == NULL) {
fprintf(stderr, "input %s has no buffer and no view_src\n", input->name);
exit(1);
GGML_ASSERT(false);
}
// FIXME: may need to use the sched buffer instead
ggml_backend_view_init(input->view_src->buffer, input);
}
if (input_cpy->buffer == NULL) {
fprintf(stderr, "input_cpy %s has no buffer\n", input_cpy->name);
exit(1);
GGML_ASSERT(false);
}
//GGML_ASSERT(input->buffer->backend != input_cpy->buffer->backend);
//GGML_ASSERT(input_cpy->buffer->backend == split_backend);
// TODO: avoid this copy if it was already copied in a previous split, and the input didn't change
// this is important to avoid copying constants such as KQ_mask and inp_pos multiple time
ggml_backend_tensor_copy(input, input_cpy);
}
// ggml_backend_synchronize(split_backend);
@ -1301,6 +1400,7 @@ void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml
}
// utils
void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
GGML_ASSERT(tensor->buffer == NULL);
//GGML_ASSERT(tensor->data == NULL); // views of pre-allocted tensors may have the data set, but still need to be initialized

25
ggml-cuda.cu
View file

@ -9712,6 +9712,7 @@ static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph
if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE)
continue;
#ifndef NDEBUG
assert(node->backend == GGML_BACKEND_GPU);
assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
assert(node->extra != nullptr);
@ -9723,35 +9724,13 @@ static bool ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph
assert(node->src[j]->extra != nullptr);
}
}
#endif
bool ok = ggml_cuda_compute_forward(&params, node);
if (!ok) {
fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
}
GGML_ASSERT(ok);
#if 0
if (node->type == GGML_TYPE_F32) {
cudaDeviceSynchronize();
std::vector<float> tmp(ggml_nelements(node), 0.0f);
cudaMemcpy(tmp.data(), node->data, ggml_nelements(node)*sizeof(float), cudaMemcpyDeviceToHost);
printf("\n%s (%s) (%s %s) (%s %s): ", node->name, ggml_op_name(node->op),
ggml_type_name(node->src[0]->type),
node->src[1] ? ggml_type_name(node->src[1]->type) : "none",
node->src[0]->name,
node->src[1] ? node->src[1]->name : "none");
double sum = 0.0;
double sq_sum = 0.0;
for (int i = 0; i < ggml_nelements(node); i++) {
printf("%f ", tmp[i]);
sum += tmp[i];
sq_sum += tmp[i]*tmp[i];
}
printf("\n");
printf("sum: %f, ", sum);
printf("sq_sum: %f\n", sq_sum);
}
#endif
}
UNUSED(backend);

11
llama.cpp
View file

@ -1730,7 +1730,6 @@ static bool llama_kv_cache_init(
return false;
}
ggml_backend_buffer_clear(buf, 0);
// FIXME: buffer type name
LLAMA_LOG_INFO("%s: %10s KV buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0);
cache.bufs.push_back(buf);
}
@ -2463,9 +2462,9 @@ struct llama_model_loader {
for (int i = 0; i < gguf_get_n_tensors(ctx_gguf); i++) {
struct ggml_tensor * cur = ggml_get_tensor(ctx, gguf_get_tensor_name(ctx_gguf, i));
if (!cur) {
// some tensors may be allocated in a different context
continue;
}
GGML_ASSERT(cur); // unused tensors should have been caught by load_data already
if (progress_callback) {
if (!progress_callback((float) size_done / size_data, progress_callback_user_data)) {
@ -3734,6 +3733,8 @@ static bool llm_load_tensors(
if (buf == nullptr) {
throw std::runtime_error("failed to allocate buffer");
}
// indicate that this buffer contains weights
// this is used by ggml_backend_sched to improve op scheduling -> ops that use a weight are always scheduled to the backend that contains the weight
ggml_backend_buffer_set_usage(buf, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
model.bufs.push_back(buf);
ctx_bufs.emplace_back(ctx, buf);
@ -4336,6 +4337,12 @@ struct llm_build_context {
cb(Vcur, "Vcur", il);
}
// these nodes are added to the graph together so that they are not reordered
// by doing so, the number of splits in the graph is reduced
ggml_build_forward_expand(gf, Qcur);
ggml_build_forward_expand(gf, Kcur);
ggml_build_forward_expand(gf, Vcur);
Qcur = ggml_rope_custom(
ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
n_embd_head, 0, 0, n_orig_ctx, freq_base, freq_scale,